Chronic obstructive pulmonary disease (COPD) is the most common chronic respiratory disease around the world, and pharmacotherapy is the foremost treatment method currently. In recent decades, with the rapid development of bronchoscopic interventional therapy, endoscopic physical ablation technology presents a therapeutic effect in treating COPD, with few treatment-related side effects, showing excellent application prospects in treating COPD. Since ablation techniques in this field are emerging technologies with low patient acceptance, they are not widely used in the clinical treatment of COPD. This article reviews the development process of physical ablation techniques. Moreover, their current application status and the prospects in the field of COPD treatment are also summarized and analyzed. We hope to promote the application of physical ablation in the clinical treatment of COPD and provide practical references and a theoretical basis for the clinical treatment of COPD.

Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge, necessitating early detection, timely diagnosis, and prompt treatment to improve patient outcomes. Endoscopic examination plays a pivotal role in this regard. However, despite the availability of various endoscopic techniques, certain limitations can result in missed or misdiagnosed ESCCs. Currently, artificial intelligence (AI)-assisted endoscopic diagnosis has made significant strides in addressing these limitations and improving the diagnosis of ESCC and precancerous lesions. In this review, we provide an overview of the current state of AI applications for endoscopic diagnosis of ESCC and precancerous lesions in aspects including lesion characterization, margin delineation, invasion depth estimation, and microvascular subtype classification. Furthermore, we offer insights into the future direction of this field, highlighting potential advancements that can lead to more accurate diagnoses and ultimately better prognoses for patients.
Humans ; Artificial Intelligence ; Esophageal Squamous Cell Carcinoma/diagnosis* ; Esophageal Neoplasms/diagnosis* ; Precancerous Conditions/diagnosis*

Coronavirus-related diseases pose a significant challenge to the global health system. Given the diversity of coronaviruses and the unpredictable nature of disease outbreaks, the traditional "one bug, one drug" paradigm struggles to address the growing number of emerging crises. Therefore, there is an urgent need for therapeutic agents with broad-spectrum anti-coronavirus activity. Here, we provide evidence that ATV006, an anti-SARS-CoV-2 nucleoside analog targeting RNA-dependent RNA polymerase (RdRp), has broad antiviral activity against human and animal coronaviruses. Using mouse hepatitis virus (MHV) and human coronavirus NL63 (HCoV-NL63) as a model, we show that ATV006 has potent prophylactic and therapeutic activity against murine coronavirus infection in vivo. Remarkably, ATV006 successfully inhibits viral replication in mice even when administered 96 h after infection. Due to its oral bioavailability and potency against multiple coronaviruses, ATV006 has the potential to become a useful antiviral agent against SARS-CoV-2 and other circulating and emerging coronaviruses in humans and animals.

Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,of-fering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accu-racy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summa-rizes the application of AI in drug development,particularly in drug-target prediction,and offers rec-ommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

Nanozyme is novel nanoparticle with enzyme-like activity, which can be classified into peroxidase-like nanozyme, catalase-like nanozyme, superoxide dismutase-like nanozyme, oxidase-like nanozyme and hydrolase-like nanozyme according to the type of reaction they catalyze. Since researchers first discovered Fe₃O₄ nanoparticles with peroxidase-like activity in 2007, a variety of nanoparticles have been successively found to have catalytic activity and applied in bioassays, inflammation control, antioxidant damage and tumor therapy, playing a key role in disease diagnosis and treatment. We summarize the use of nanozymes with different classes of enzymatic activity in the diagnosis and treatment of diseases and describe the main factors influencing nanozyme activity. A Mn-based peroxidase-like nanozyme that induces the reduction of glutathione in tumors to produce glutathione disulfide and Mn²⁺, which induces the production of reative oxygen species (ROS) in tumor cells by breaking down H₂O₂ in physiological media through Fenton-like action, thereby inhibiting tumor cell growth. To address the limitation of tumor tissue hypoxia during photodynamic tumor therapy, the effect of photodynamic therapy is significantly enhanced by using hydrogen peroxide nanozymes to catalyze the production of oxygen from H₂O₂. In pathological states, where excess superoxide radicals are produced in the body, superoxide dismutase-like nanozymes are able to selectively regulate intracellular ROS levels, thereby protecting normal cells and slowing down the degradation of cellular function. Based on this principle, an engineered nanosponge has been designed to rapidly scavenge free radicals and deliver oxygen in time to save nerve cells before thrombolysis. Starvation therapy, in which glucose oxidase catalyzes the hydrolysis of glucose to gluconic acid and hydrogen peroxide in cancer cells with the involvement of oxygen, attenuates glycolysis and the production of intermediate metabolites such as nucleotides, lipids and amino acids, was used to synthesize an oxidase-like nanozyme that achieved effective inhibition of tumor growth. Furthermore, by fine-tuning the Lewis acidity of the metal cluster to improve the intrinsic activity of the hydrolase nanozyme and providing a shortened ligand length to increase the density of its active site, a hydrolase-like nanozyme was successfully synthesized that is capable of cleaving phosphate bonds, amide bonds, glycosidic bonds and even biofilms with high efficiency in hydrolyzing the substrate. All these effects depend on the size, morphology, composition, surface modification and environmental media of the nanozyme, which are important aspects to consider in order to improve the catalytic efficiency of the nanozyme and have important implications for the development of nanozyme. Although some progress has been made in the research of nanozymes in disease treatment and diagnosis, there are still some problems, for example, the catalytic rate of nanozymes is still difficult to reach the level of natural enzymes in vivo, and the toxic effects of some heavy metal nanozymes material itself. Therefore, the construction of nanozyme systems with multiple functions, good biocompatibility and high targeting efficiency, and their large-scale application in diagnosis and treatment is still an urgent problem to be solved. (1) To improve the selectivity and specificity of nanozymes. By using antibody coupling, the nanoparticles are able to specifically bind to antigens that are overexpressed in certain cancer cells. It also significantly improves cellular internalization through antigen-mediated endocytosis and enhances the enrichment of nanozymes in target tissues, thereby improving targeting during tumor therapy. Some exogenous stimuli such as laser and ultrasound are used as triggers to control the activation of nanozymes and achieve specific activation of nanozyme. (2) To explore more practical and safer nanozymes and their catalytic mechanisms: biocompatible, clinically proven material molecules can be used for the synthesis of nanoparticles. (3) To solve the problem of its standardization and promote the large-scale clinical application of nanozymes in biomonitoring. Thus, it can go out of the laboratory and face the market to serve human health in more fields, which is one of the future trends of nanozyme development.

ObjectiveTePixD (Tll0078) is a blue light-using flavin (BLUF) photoreceptor protein from Thermosynechococcus elongatus BP-1. TePixD protein has a conserved Tyr8-Gln50-Met93 triad around the FAD pocket to mediate the proton-coupled electron transfer (PCET) process. But the detailed light response mechanism needs further study. We aimed to elucidate the structure and biochemical properties of TePixD mutants at key light response sites to analyze the light response process of TePixD. MethodsWe employed X-ray crystallography to resolve the crystal structure of the TePixD Y8F mutant. The side chain of Tyr8 is involved in PCET while Phe8 in mutation loses the function due to the loss of its hydroxyl group. We compared the structure of TePixD Y8F mutation to TePixD wild type (WT) and its homology protein SyPixD Y8F. Using multi-angle light scattering (MALS), we analyzed the oligomerization of multiple TePixD mutations (Y8F, Q50L, W91F, Y8F/W91F, and Q50L/W91F), focusing specifically on mutational sites that are critical residues for the protein’s photo response to dark and light conditions. ResultsWe resolved the crystal structure of TePixD Y8F mutant at a resolution of 2.54 Å and found that it shares a similar overall structure with the TePixD WT but exhibits significant differences from the SyPixD Y8F structure. Biochemical analysis revealed differences in molecular mass and elution profiles between the TePixD mutants and the WT under dark and light conditions, indicating the perturbation on the light-induced conformational change by the mutants. ConclusionOur structure determination and biochemical analyses will add information to reveal the light response mechanism of BLUF proteins.

Humans ; Lymph Nodes/pathology* ; Lymph Node Excision ; Neoplasms/pathology* ; Dissection

Humans ; Female ; Pregnancy ; Placenta ; Lung/pathology* ; Tomography, X-Ray Computed